Abstract
Optimization algorithms have made considerable advancements in solving complex problems with the ability to be applied to innumerable real-world problems. Nevertheless, they are passed through several challenges comprising of equilibrium between exploration and exploitation capabilities, and departure from local optimums. Portioning the population into several sub-populations is a robust technique to enhance the dispersion of the solution in the problem space. Consequently, the exploration would be increased, and the local optimums can be avoided. Furthermore, improving the exploration and exploitation capabilities is a way of increasing the authority of optimization algorithms that various researches have been considered, and numerous methods have been proposed. In this paper, a novel hybrid multi-population algorithm called HMPA is presented. First, a new portioning method is introduced to divide the population into several sub-populations. The sub-populations dynamically exchange solutions aiming at balancing the exploration and exploitation capabilities. Afterthought, artificial ecosystem-based optimization (AEO) and Harris Hawks optimization (HHO) algorithms are hybridized. Subsequently, levy-flight strategy, local search mechanism, quasi-oppositional learning, and chaos theory are utilized in a splendid way to maximize the efficiency of the HMPA. Next, HMPA is evaluated on fifty unimodal, multimodal, fix-dimension, shifted rotated, hybrid, and composite test functions. In addition, the results of HMPA is compared with similar state-of-the-art algorithms using five well-known statistical metrics, box plot, convergence rate, execution time, and Wilcoxon’s signed-rank test. Finally, the performance of the HMPA is investigated on seven constrained/unconstrained real-life engineering problems. The results demonstrate that the HMPA is outperformed the other competitor algorithms significantly.
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Barshandeh S, Haghzadeh M (2020) A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems. Eng Comput. https://doi.org/10.1007/s00366-020-00994-0
Chandrawat RK, Kumar R, Garg B, Dhiman G, Kumar S (2017) An analysis of modeling and optimization production cost through fuzzy linear programming problem with symmetric and right angle triangular fuzzy number. In: Proceedings of sixth international conference on soft computing for problem solving. Springer, pp 197–211
Kaur A, Dhiman G (2019) A review on search-based tools and techniques to identify bad code smells in object-oriented systems. In: Yadav N, Yadav A, Bansal J, Deep K, Kim J (eds) Harmony search and nature inspired optimization algorithms, vol 741. Springer, Singapore. https://doi.org/10.1007/978-981-13-0761-4_86
Moghdani R, Abd Elaziz M, Mohammadi D et al (2020) An improved volleyball premier league algorithm based on sine cosine algorithm for global optimization problem. Eng Comput. https://doi.org/10.1007/s00366-020-00962-8
Sattar D, Salim R (2020) A smart metaheuristic algorithm for solving engineering problems. Eng Comput. https://doi.org/10.1007/978-3-030-16339-6_5
Zhang Y, Jin Z (2020) Group teaching optimization algorithm: a novel metaheuristic method for solving global optimization problems. Expert Syst Appl 148:113246
Cuevas E, Fausto F, González A (2020) The locust swarm optimization algorithm. In: New advancements in swarm algorithms: operators and applications. Springer, pp 139–159
Zhao W, Zhang Z, Wang L (2020) Manta ray foraging optimization: an effective bio-inspired optimizer for engineering applications. Eng Appl Artif Intell 87:103300
Zhao W, Wang L, Zhang Z (2019) Supply-demand-based optimization: a novel economics-inspired algorithm for global optimization. IEEE Access 7:73182–73206
Yadav A (2019) AEFA: artificial electric field algorithm for global optimization. Swarm Evolut Comput 48:93–108
Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020) Equilibrium optimizer: a novel optimization algorithm. Knowl-Based Syst 191:105190
Dhiman G, Kaur A (2019) STOA: a bio-inspired based optimization algorithm for industrial engineering problems. Eng Appl Artif Intell 82:148–174
Hashim FA, Houssein EH, Mabrouk MS, Al-Atabany W, Mirjalili S (2019) Henry gas solubility optimization: a novel physics-based algorithm. Future Gener Comput Syst 101:646–667
Abdullah JM, Ahmed T (2019) Fitness dependent optimizer: inspired by the bee swarming reproductive process. IEEE Access 7:43473–43486
Mohamed AAA, Hassan SA, Hemeida AM, Alkhalaf S, Mahmoud MMM, Baha Eldin AM (2019) Parasitism–Predation algorithm (PPA): a novel approach for feature selection. Ain Shams Eng J 11(2):293–308. https://doi.org/10.1016/j.asej.2019.10.004
Dhiman G, Kumar V (2018) Emperor penguin optimizer: a bio-inspired algorithm for engineering problems. Knowl-Based Syst 159:20–50
Dhiman G, Kumar V (2018) Multi-objective spotted hyena optimizer: a multi-objective optimization algorithm for engineering problems. Knowl-Based Syst 150:175–197
Dhiman G, Kumar V (2019) Seagull optimization algorithm: theory and its applications for large-scale industrial engineering problems. Knowl-Based Syst 165:169–196
Dhiman G, Kaur A Spotted hyena optimizer for solving engineering design problems. In: 2017 international conference on machine learning and data science (MLDS), 2017. IEEE, pp 114–119
Dhiman G, Kumar V (2019) Spotted hyena optimizer for solving complex and non-linear constrained engineering problems. In: Yadav N, Yadav A, Bansal J, Deep K, Kim J (eds) Harmony search and nature inspired optimization algorithms, vol 741. Springer, Singapore. https://doi.org/10.1007/978-981-13-0761-4_81
Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst Appl 152:113377. https://doi.org/10.1016/j.eswa.2020.113377
Singh P, Dhiman G (2018) A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches. J Comput Sci 27:370–385
Singh P, Dhiman G (2017) A fuzzy-LP approach in time series forecasting. In: International conference on pattern recognition and machine intelligence. Springer, pp 243–253
Singh P, Dhiman G, Kaur A (2018) A quantum approach for time series data based on graph and Schrödinger equations methods. Mod Phys Lett A 33(35):1850208
Dhiman G, Kaur A (2018) Optimizing the design of airfoil and optical buffer problems using spotted hyena optimizer. Designs 2(3):28
Dhiman G, Guo S, Kaur S (2018) ED-SHO: a framework for solving nonlinear economic load power dispatch problem using spotted hyena optimizer. Mod Phys Lett A 33(40):1850239
Kaur A, Kaur S, Dhiman G (2018) A quantum method for dynamic nonlinear programming technique using Schrödinger equation and Monte Carlo approach. Mod Phys Lett B 32(30):1850374
Singh P, Rabadiya K, Dhiman G (2018) A four-way decision-making system for the Indian summer monsoon rainfall. Mod Phys Lett B 32(25):1850304
Singh P, Dhiman G (2018) Uncertainty representation using fuzzy-entropy approach: special application in remotely sensed high-resolution satellite images (RSHRSIs). Appl Soft Comput 72:121–139
Dhiman G, Kumar V (2018) Astrophysics inspired multi-objective approach for automatic clustering and feature selection in real-life environment. Mod Phys Lett B 32(31):1850385
Zhao W, Wang L, Zhang Z (2020) Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. Neural Comput Appl 32:9383–9425. https://doi.org/10.1007/s00521-019-04452-x
Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849–872
Ma Y, Bai Y (2020) A multi-population differential evolution with best-random mutation strategy for large-scale global optimization. Appl Intell 50:1510–1526. https://doi.org/10.1007/s10489-019-01613-2
Babalik A (2018) A novel multi-swarm approach for numeric optimization. Int J Intell Syst Appl Eng 6(3):220–227
Ye W, Feng W, Fan S (2017) A novel multi-swarm particle swarm optimization with dynamic learning strategy. Appl Soft Comput 61:832–843
Arora S, Anand P (2019) Chaotic grasshopper optimization algorithm for global optimization. Neural Comput Appl 31(8):4385–4405
Demir FB, Tuncer T, Kocamaz AF (2020) A chaotic optimization method based on logistic-sine map for numerical function optimization. Neural Comput Appl. https://doi.org/10.1007/s00521-020-04815-9
Zhang X, Xu Y, Yu C, Heidari AA, Li S, Chen H, Li C (2020) Gaussian mutational chaotic fruit fly-built optimization and feature selection. Expert Syst Appl 141:112976
Dhiman G (2019) ESA: a hybrid bio-inspired metaheuristic optimization approach for engineering problems. Eng Comput. https://doi.org/10.1007/s00366-019-00826-w
Gupta S, Deep K, Moayedi H et al (2020) Sine cosine grey wolf optimizer to solve engineering design problems. Eng Comput. https://doi.org/10.1007/s00366-020-00996-y
Kaur S, Awasthi LK, Sangal AL (2020) HMOSHSSA: a hybrid meta-heuristic approach for solving constrained optimization problems. Eng Comput. https://doi.org/10.1007/s00366-020-00989-x
Shehab M, Alshawabkah H, Abualigah L, Nagham A-M (2020) Enhanced a hybrid moth-flame optimization algorithm using new selection schemes. Eng Comput. https://doi.org/10.1007/s00366-020-00971-7
Dhiman G, Kaur A (2019) A hybrid algorithm based on particle swarm and spotted hyena optimizer for global optimization. In: Bansal J, Das K, Nagar A, Deep K, Ojha A (eds) Soft computing for problem solving, vol 816. Springer, Singapore, pp 599–615. https://doi.org/10.1007/978-981-13-1592-3_47
Dhiman G, Kumar V (2019) KnRVEA: a hybrid evolutionary algorithm based on knee points and reference vector adaptation strategies for many-objective optimization. Appl Intell 49(7):2434–2460
Debnath S, Baishya S, Sen D et al (2020) A hybrid memory-based dragonfly algorithm with differential evolution for engineering application. Eng Comput. https://doi.org/10.1007/s00366-020-00958-4
Parouha RP, Das KN (2016) A memory based differential evolution algorithm for unconstrained optimization. Appl Soft Comput 38:501–517
Sree Ranjini KS, Murugan S (2017) Memory based hybrid dragonfly algorithm for numerical optimization problems. Expert Syst Appl 83:63–78
Cheng J, Wang L, Xiong Y (2019) Cuckoo search algorithm with memory and the vibrant fault diagnosis for hydroelectric generating unit. Eng Comput 35(2):687–702
Gupta S, Deep K (2019) Enhanced leadership-inspired grey wolf optimizer for global optimization problems. Eng Comput. https://doi.org/10.1007/s00366-019-00795-0
Zhou Z, Li F, Zhu H, Xie H et al (2020) An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04119-7
Roslan NB (2019) Lecturer timetable optimizer using genetic algorithm with hill climbing optimization method (LETO 2.0). Submitted in fulfilment of the requirement for Bachelor of Information Technology (Hons.), Intelligent System Engineering Faculty of Computer and Mathematical Science
Kesavan S, Sivaraj K, Palanisamy A, Murugasamy R (2019) Distributed localization algorithm using hybrid cuckoo search with hill climbing (CS-HC) algorithm for internet of things. Int J Psychosoc Rehabil 23(4)
Rao RV, Keesari HS, Oclon P, Taler J (2020) An adaptive multi-team perturbation-guiding Jaya algorithm for optimization and its applications. Eng Comput 36(1):391–419
Ang KM, Lim WH, Isa NAM, Tiang SS, Wong CH (2020) A constrained multi-swarm particle swarm optimization without velocity for constrained optimization problems. Expert Syst Appl 140:112882
Rao R, Pawar R (2020) Self-adaptive multi-population Rao algorithms for engineering design optimization. Appl Artif Intell 34(3):187–250
Vafashoar R, Meybodi MR (2020) A multi-population differential evolution algorithm based on cellular learning automata and evolutionary context information for optimization in dynamic environments. Appl Soft Comput 88:106009
Chen H, Heidari AA, Zhao X, Zhang L, Chen H (2020) Advanced orthogonal learning-driven multi-swarm sine cosine optimization: framework and case studies. Expert Syst Appl 144:113113
Darwish A, Ezzat D, Hassanien AE (2020) An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis. Swarm Evolut Comput 52:100616
Xu Z, Hu Z, Heidari AA, Wang M, Zhao X, Chen H, Cai X (2020) Orthogonally-designed adapted grasshopper optimization: a comprehensive analysis. Expert Syst Appl 150:113282
Ozsoydan FB, Baykasoğlu A (2019) Quantum firefly swarms for multimodal dynamic optimization problems. Expert Syst Appl 115:189–199
Vijay RK, Nanda SJ (2019) A Quantum Grey Wolf Optimizer based declustering model for analysis of earthquake catalogs in an ergodic framework. J Comput Sci 36:101019
Liao Y, Qin G, Liu F (2019) Particle swarm optimization research base on quantum self-learning behavior. J Comput Methods Sci Eng. https://doi.org/10.3233/JCM-193644
Turgut MS, Turgut OE (2020) Global best-guided oppositional algorithm for solving multidimensional optimization problems. Eng Comput 36(1):43–73
Xu Y, Yang Z, Li X, Kang H, Yang X (2020) Dynamic opposite learning enhanced teaching–learning-based optimization. Knowl-Based Syst 188:104966
Chen H, Jiao S, Heidari AA, Wang M, Chen X, Zhao X (2019) An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models. Energy Convers Manag 195:927–942
Aslan S (2019) Time-based information sharing approach for employed foragers of artificial bee colony algorithm. Soft Comput 23(16):7471–7494
Tian M, Gao X (2019) An improved differential evolution with information intercrossing and sharing mechanism for numerical optimization. Swarm Evolut Comput 50:100341
Ning Y, Peng Z, Dai Y, Bi D, Wang J (2019) Enhanced particle swarm optimization with multi-swarm and multi-velocity for optimizing high-dimensional problems. Appl Intell 49(2):335–351
Truong KH, Nallagownden P, Baharudin Z, Vo DN (2019) A quasi-oppositional-chaotic symbiotic organisms search algorithm for global optimization problems. Appl Soft Comput 77:567–583
Guha D, Roy P, Banerjee S (2019) Quasi-oppositional backtracking search algorithm to solve load frequency control problem of interconnected power system. Iran J Sci Technol Trans Electr Eng 44:781–804. https://doi.org/10.1007/s40998-019-00260-0
Shiva CK, Kumar R (2020) Quasi-oppositional harmony search algorithm approach for ad hoc and sensor networks. In: De D, Mukherjee A, Kumar Das S, Dey N (eds) Nature inspired computing for wireless sensor networks. Springer tracts in nature-inspired computing. Springer, Singapore. Springer, pp 175–194. https://doi.org/10.1007/978-981-15-2125-6_9
Yi J, Li X, Chu C-H, Gao L (2019) Parallel chaotic local search enhanced harmony search algorithm for engineering design optimization. J Intell Manuf 30(1):405–428
Gao S, Yu Y, Wang Y, Wang J, Cheng J, Zhou M (2019) Chaotic local search-based differential evolution algorithms for optimization. IEEE Trans Syst Man Cybern Syst. https://doi.org/10.1109/TSMC.2019.2956121
Zhao R, Wang Y, Liu C, Hu P, Li Y, Li H, Yuan C (2020) Selfish herd optimizer with levy-flight distribution strategy for global optimization problem. Physica A 538:122687
Xie W, Wang J, Tao Y (2019) Improved black hole algorithm based on golden sine operator and levy flight operator. IEEE Access 7:161459–161486
Wu H, Wu P, Xu K, Li F (2020) Finite element model updating using crow search algorithm with levy flight. Int J Numer Methods Eng. https://doi.org/10.1002/nme.6338
Qiu C (2019) A novel multi-swarm particle swarm optimization for feature selection. Genet Program Evolvable Mach 20(4):503–529
Sedarous S, El-Gokhy SM, Sallam E (2018) Multi-swarm multi-objective optimization based on a hybrid strategy. Alexandria Eng J 57(3):1619–1629
Nie W, Xu L Multi-swarm hybrid optimization algorithm with prediction strategy for dynamic optimization problems. In: 2016 international forum on mechanical, control and automation (IFMCA 2016), 2017. Atlantis Press
Li J, Xiao D-d, Zhang T, Liu C, Li Y-x, Wang G-g (2020) Multi-swarm cuckoo search algorithm with Q-learning model. Comput J. https://doi.org/10.1093/comjnl/bxz149
Ali MZ, Awad NH, Suganthan PN (2015) Multi-population differential evolution with balanced ensemble of mutation strategies for large-scale global optimization. Appl Soft Comput 33:304–327
Biswas S, Das S, Debchoudhury S, Kundu S (2014) Co-evolving bee colonies by forager migration: a multi-swarm based Artificial Bee Colony algorithm for global search space. Appl Math Comput 232:216–234
Di Carlo M, Vasile M, Minisci E (2020) Adaptive multi-population inflationary differential evolution. Soft Comput 24:3861–3891. https://doi.org/10.1007/s00500-019-04154-5
Xiang Y, Zhou Y (2015) A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization. Appl Soft Comput 35:766–785
Bao H, Han F A hybrid multi-swarm PSO algorithm based on shuffled frog leaping algorithm. In: International conference on intelligent science and big data engineering, 2017. Springer, pp 101–112
Wu G, Mallipeddi R, Suganthan PN, Wang R, Chen H (2016) Differential evolution with multi-population based ensemble of mutation strategies. Inf Sci 329:329–345
Saha S, Mukherjee V (2018) A novel quasi-oppositional chaotic antlion optimizer for global optimization. Appl Intell 48(9):2628–2660
Dhiman G, Kumar V (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70
Shayanfar H, Gharehchopogh FS (2018) Farmland fertility: a new metaheuristic algorithm for solving continuous optimization problems. Appl Soft Comput 71:728–746
Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191
Mirjalili S (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl-Based Syst 89:228–249
Mirjalili S (2015) The ant lion optimizer. Adv Eng Softw 83:80–98
Awad N, Ali M, Liang J, Qu B, Suganthan P (2017) CEC 2017 Special session on single objective numerical optimization single bound constrained real-parameter numerical optimization
Liang J, Qu B, Suganthan P (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, p 635
Kiran MS (2015) TSA: tree-seed algorithm for continuous optimization. Expert Syst Appl 42(19):6686–6698
Mirjalili S (2016) SCA: a sine cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
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Barshandeh, S., Piri, F. & Sangani, S.R. HMPA: an innovative hybrid multi-population algorithm based on artificial ecosystem-based and Harris Hawks optimization algorithms for engineering problems. Engineering with Computers 38, 1581–1625 (2022). https://doi.org/10.1007/s00366-020-01120-w
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DOI: https://doi.org/10.1007/s00366-020-01120-w